

"""simple docstring"""




from __future__ import annotations

from typing import Any







class   __lowercase      :

  def __init__(	self		:      int       ,A		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       num_of_nodes
   UpperCAmelCase__       :	list[list[int]]        =       []
   UpperCAmelCase__       :	dict[int, int]        =       {}

  def  __lowercase							(	self		:      Any       ,A		:      int       ,A		:      int       ,A		:      int     ):






   '''simple docstring'''
   self.m_edges.append([u_node, v_node, weight]     )

  def  __lowercase							(	self		:      Tuple       ,A		:      int     ):






   '''simple docstring'''

   if self.m_component[u_node] == u_node:
    return u_node
   return self.find_component(self.m_component[u_node]     )

  def  __lowercase							(	self		:      List[Any]       ,A		:      int     ):






   '''simple docstring'''

   if self.m_component[u_node] != u_node:
    for k in self.m_component:
     UpperCAmelCase__       :	List[Any]        =       self.find_component(A     )

  def  __lowercase							(	self		:      List[str]       ,A		:      list[int]       ,A		:      int       ,A		:      int     ):






   '''simple docstring'''

   if component_size[u_node] <= component_size[v_node]:
    UpperCAmelCase__       :	Any        =       v_node
    component_size[v_node] += component_size[u_node]
    self.set_component(A     )

   elif component_size[u_node] >= component_size[v_node]:
    UpperCAmelCase__       :	List[str]        =       self.find_component(A     )
    component_size[u_node] += component_size[v_node]
    self.set_component(A     )




  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       []
   UpperCAmelCase__       :	str        =       0

   UpperCAmelCase__       :	list[Any]        =       [-1] * self.m_num_of_nodes

   # A list of components (initialized to all of the nodes)
   for node in range(self.m_num_of_nodes     ):
    self.m_component.update({node: node}     )
    component_size.append(1     )

   UpperCAmelCase__       :	Tuple        =       self.m_num_of_nodes

   while num_of_components > 1:
    for edge in self.m_edges:
     UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[int]        =       edge

     UpperCAmelCase__       :	Tuple        =       self.m_component[u]
     UpperCAmelCase__       :	Optional[int]        =       self.m_component[v]

     if u_component != v_component:

      for component in (u_component, v_component):
       if (
           minimum_weight_edge[component] == -1
           or minimum_weight_edge[component][2] > w
       ):
        UpperCAmelCase__       :	Optional[int]        =       [u, v, w]

    for edge in minimum_weight_edge:
     if isinstance(A       ,A     ):
      UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Tuple        =       edge

      UpperCAmelCase__       :	str        =       self.m_component[u]
      UpperCAmelCase__       :	List[str]        =       self.m_component[v]

      if u_component != v_component:
       mst_weight += w
       self.union(A       ,A       ,A     )
       print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n"     )
       num_of_components -= 1

    UpperCAmelCase__       :	Union[str, Any]        =       [-1] * self.m_num_of_nodes
   print(f"The total weight of the minimal spanning tree is: {mst_weight}"     )







def 	lowerCAmelCase    (       ):
 '''simple docstring'''







if __name__ == "__main__":
      import doctest

      doctest.testmod()






"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
